3 citations · 4 across the 5 of their papers we have counts for
5 papers
Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment
Haozhong Wang, Zhuo Li, Yibo Yang +3
The inherent safety alignment of Large Language Models (LLMs) is prone to erosion during fine-tuning, even when using seemingly innocuous datasets. While existing defenses attempt…
Deep Neural Network Calibration by Reducing Classifier Shift with Stochastic Masking
Jiani Ni, He Zhao, Yibo Yang +1
In recent years, deep neural networks (DNNs) have shown competitive results in many fields. Despite this success, they often suffer from poor calibration, especially in safety-crit…
Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration
Jiani Ni, He Zhao, Jintong Gao +2
In recent years, deep neural networks (DNNs) have demonstrated state-of-the-art performance across various domains. However, despite their success, they often face calibration issu…
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
Hangting Ye, Wei Fan, Xiaozhuang Song +4
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. With the recent success of deep learning, many t…
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
Zhuo Li, He Zhao, Zhen Li +3
Real-world datasets usually are class-imbalanced and corrupted by label noise. To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim…